Technical Abstract:
Developing renewable fuels is receiving increased emphasis due to the current energy situation. Farm-based corps, other plant materials and agricultural wastes have been considered as renewable energy sources to produce ethanol. Barley is one of the potential ethanol sources. For efficient ethanol production, it is important to know the amount of fermentable and other components in barley. Also, it is important to develop a non-destructive and rapid method to analyze for these components. This study was conducted to investigate the potential of NIR spectroscopy as a rapid and non-destructive analytical technique. A total of 142 barley samples with various varieties, growing locations, types were collected and samples were prepared as flour and kernel types. Moisture, starch, and protein were analyzed as major components. NIR data were collected on FT-NIR and dispersive NIR, and the results compared. Principal component analysis and partial least squares regression were performed using Matlab (ver 7.01) with PLS_Toolbox (ver. 3.5). PLS models using the kernel samples resulted in acceptable error levels, giving prediction errors: 0.5%, 1.7%, and 0.5% for moisture, starch, and protein (respectively), even though the accuracy was slightly lower than that by flour samples. Model performance between instruments was comparable.